Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks
2020435 citationsTingyang Xu, Peilin Zhao et al.Proceedings of the AAAI Conference on Artificial Intelligenceprofile →
Graph Representation Learning via Graphical Mutual Information Maximization
2020339 citationsWenbing Huang, Yu Rong et al.profile →
Progressive Feature Alignment for Unsupervised Domain Adaptation
2019299 citationsWenbing Huang, Yu Rong et al.profile →
A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets
202472 citationsLei Huang, Tingyang Xu et al.Nature Communicationsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Tingyang Xu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tingyang Xu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tingyang Xu more than expected).
This network shows the impact of papers produced by Tingyang Xu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tingyang Xu. The network helps show where Tingyang Xu may publish in the future.
Co-authorship network of co-authors of Tingyang Xu
This figure shows the co-authorship network connecting the top 25 collaborators of Tingyang Xu.
A scholar is included among the top collaborators of Tingyang Xu based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Tingyang Xu. Tingyang Xu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Huang, Lei, Tingyang Xu, Yang Yu, et al.. (2024). A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets. Nature Communications. 15(1). 2657–2657.72 indexed citations breakdown →
Chang, Heng, Yu Rong, Tingyang Xu, et al.. (2021). Not All Low-Pass Filters are Robust in Graph Convolutional Networks. Neural Information Processing Systems. 34.14 indexed citations
Rong, Yu, Yatao Bian, Tingyang Xu, et al.. (2020). Self-Supervised Graph Transformer on Large-Scale Molecular Data. Neural Information Processing Systems. 33. 12559–12571.12 indexed citations
15.
Rong, Yu, Wenbing Huang, Tingyang Xu, & Junzhou Huang. (2019). The Truly Deep Graph Convolutional Networks for Node Classification. arXiv (Cornell University).12 indexed citations
16.
Chang, Heng, Yu Rong, Tingyang Xu, et al.. (2019). The General Black-box Attack Method for Graph Neural Networks.. arXiv (Cornell University).3 indexed citations
17.
Cai, Xingyu, Tingyang Xu, Jinfeng Yi, Junzhou Huang, & Sanguthevar Rajasekaran. (2019). DTWNet: a Dynamic Time Warping Network. Neural Information Processing Systems. 32. 11636–11646.36 indexed citations
18.
Rong, Yu, Wenbing Huang, Tingyang Xu, & Junzhou Huang. (2019). DropEdge: Towards the Very Deep Graph Convolutional Networks for Node Classification. arXiv (Cornell University).5 indexed citations
19.
Sun, Jiangwen, Jin Lü, Tingyang Xu, & Jinbo Bi. (2015). Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization. International Conference on Machine Learning. 757–766.34 indexed citations
20.
Bi, Jinbo, Tingyang Xu, Chi-Ming Chen, & Jason Johannesen. (2015). Spatio-Temporal Modeling of EEG Data for Understanding Working Memory.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.